The L2RM develops a low-rank linear regression model to correlate a high-dimensional response matrix with a high-dimensional vector of covariates when coefficient matrices have low-rank structures. For more detailed information about the modeling and method, please refer to 

Dehan Kong, Baiguo An, Jingwen Zhang and Hongtu Zhu. (2019+). L2RM: Low-rank Linear Regression Models for High-dimensional Matrix Responses. Journal of the American Statistical Association, to appear. 

We include the simulation codes used in the paper. 

The main function for the first step screening procedure is included in the “screeningmatrix.m” file. 
The main function for the second step estimation procedure is included in the “regularizedmatrixcvestimation.m” file. 

There is also an example file “example.m” showing how to use these functions.